1. Field
Certain aspects of the present disclosure generally relate to signal processing and, more particularly, to a method and apparatus for denoising of sensed physiological signals.
2. Background
Recent advances in mobile computing and energy-efficient communication have shown promise in the continuous acquisition, storage, and processing of physiological signals. Pervasive sensors deployed in next generation networks have enabled algorithms capable of efficient and accurate information processing. However, sensors that monitor dynamic physical systems present large volumes of noisy data with a wide range of signal artifacts. For example, in ambulatory electrocardiogram (ECG) signal monitoring, artifacts can result from the movement of electrodes on the surface of the skin, power line interference, muscle noise, and baseline-wander. Direct processing of these noisy signals suffers from poor performance of algorithms such as those used for beat detection and arrhythmia classification. Efficient techniques for the selective removal of artifact sources while retaining useful signal components are thus desirable to ensure accurate performance of algorithms which rely on ambulatory ECG data.
In the past, analog and digital filtering techniques have demonstrated great success in mitigating several noise sources in the ECG. However, motion artifacts and muscle noise present a unique challenge to these signal processing methods. Linear and non-linear denoising filters fail to effectively remove interference from electrode motion and muscle noise since these represent sources which are within the same frequency range as the cardiac signal. Traditional filtering techniques applied to the noisy recordings may thus eliminate useful ECG components corrupting the signal morphology and spectral content.